Transform of visual analogue scale data and their clustering
by Kotoe Katayama; Rui Yamaguchi; Seiya Imoto; Keiko Matsuura; Kenji Watanabe; Satoru Miyano
International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP), Vol. 3, No. 2, 2011

Abstract: We propose a hierarchical clustering for the visual analogue scale (VAS) in the framework of symbolic data analysis (SDA). The VAS is a method that can be readily understood by most people to measure a characteristic or attitude that cannot be directly measured. VAS is of most value when looking at change within the same people, and is of less value for comparing across a group of people because they have different sense. It could be argued that a VAS is trying to produce interval/ratio data out of subjective values that are at best ordinal. Thus, some caution is required in handling VAS. We describe VAS as distribution and handle it as new type data in SDA. SDA was proposed by Diday at the end of the 1980s and is a new approach for analysing huge and complex data. In SDA, an observation is described by not only numerical values but also 'higher-level units'; sets, intervals, distributions, etc. In this paper, we define 'VAS distribution' and 'VAS changes distribution' as new type data in SDA and propose a hierarchical clustering for these new type data.

Online publication date: Sat, 07-Mar-2015

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